Daniel Sheldon

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 68

Top Topics

Graphical Models

Exact Inference

Differentially Private

Top Venues

Publications

Miguel Fuentes, Brett C. Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data. AISTATS (2024)
Miguel Fuentes, Brett Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data. CoRR (2024)
Gustavo Pérez, Subhransu Maji, Daniel Sheldon
DISCount: Counting in Large Image Collections with Detector-Based Importance Sampling. AAAI (2024)
Cecilia Ferrando, Daniel Sheldon
Private Regression via Data-Dependent Sufficient Statistic Perturbation. CoRR (2024)
Jinlin Lai, Javier Burroni, Hui Guan, Daniel Sheldon
Automatically marginalized MCMC in probabilistic programming. ICML (2023)
Gustavo Pérez, Daniel Sheldon, Grant Van Horn, Subhransu Maji
Human in-the-Loop Estimation of Cluster Count in Datasets via Similarity-Driven Nested Importance Sampling. CoRR (2023)
Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon
U-Statistics for Importance-Weighted Variational Inference. CoRR (2023)
Mohit Yadav, Daniel Sheldon, Cameron Musco
Kernel Interpolation with Sparse Grids. CoRR (2023)
Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon
U-Statistics for Importance-Weighted Variational Inference. Trans. Mach. Learn. Res. 2023 (2023)
Gustavo Pérez, Subhransu Maji, Daniel Sheldon
DISCount: Counting in Large Image Collections with Detector-Based Importance Sampling. CoRR (2023)
Javier Burroni, Justin Domke, Daniel Sheldon
Sample Average Approximation for Black-Box VI. CoRR (2023)
Jinlin Lai, Javier Burroni, Hui Guan, Daniel Sheldon
Automatically Marginalized MCMC in Probabilistic Programming. CoRR (2023)
Ryan McKenna, Brett Mullins, Daniel Sheldon, Gerome Miklau
AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data. Proc. VLDB Endow. 15 (11) (2022)
Cecilia Ferrando, Shufan Wang, Daniel Sheldon
Parametric Bootstrap for Differentially Private Confidence Intervals. AISTATS (2022)
Ryan McKenna, Brett Mullins, Daniel Sheldon, Gerome Miklau
AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data. CoRR (2022)
Jinlin Lai, Justin Domke, Daniel Sheldon
Variational Marginal Particle Filters. AISTATS (2022)
Ryan McKenna, Siddhant Pradhan, Daniel Sheldon, Gerome Miklau
Relaxed Marginal Consistency for Differentially Private Query Answering. CoRR (2021)
Mohit Yadav, Daniel Sheldon, Cameron Musco
Faster Kernel Interpolation for Gaussian Processes. CoRR (2021)
Shiv Shankar, Daniel Sheldon
Sibling Regression for Generalized Linear Models. CoRR (2021)
Ryan McKenna, Gerome Miklau, Daniel Sheldon
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data. J. Priv. Confidentiality 11 (3) (2021)
Zezhou Cheng, Subhransu Maji, Daniel Sheldon
AI for conservation: learning to track birds with radar. XRDS 27 (4) (2021)
Shiv Shankar, Daniel Sheldon
Sibling Regression for Generalized Linear Models. ECML/PKDD (2) (2021)
Ryan McKenna, Siddhant Pradhan, Daniel Sheldon, Gerome Miklau
Relaxed Marginal Consistency for Differentially Private Query Answering. NeurIPS (2021)
Jinlin Lai, Daniel Sheldon, Justin Domke
Variational Marginal Particle Filters. CoRR (2021)
Cheng Gu, Erik G. Learned-Miller, Daniel Sheldon, Guillermo Gallego, Pia Bideau
The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data. ICCV (2021)
Shiv Shankar, Daniel Sheldon, Tao Sun, John Pickering, Thomas G. Dietterich
Three-quarter Sibling Regression for Denoising Observational Data. CoRR (2021)
Mohit Yadav, Daniel Sheldon, Cameron Musco
Faster Kernel Interpolation for Gaussian Processes. AISTATS (2021)
Cheng Gu, Erik G. Learned-Miller, Daniel Sheldon, Guillermo Gallego, Pia Bideau
The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data. CoRR (2021)
Ryan McKenna, Gerome Miklau, Daniel Sheldon
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data. CoRR (2021)
Zezhou Cheng, Saadia Gabriel, Pankaj Bhambhani, Daniel Sheldon, Subhransu Maji, Andrew Laughlin, David Winkler
Detecting and Tracking Communal Bird Roosts in Weather Radar Data. CoRR (2020)
Abhinav Agrawal, Daniel Sheldon, Justin Domke
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization. CoRR (2020)
Ryan McKenna, Daniel Sheldon
Permute-and-Flip: A new mechanism for differentially private selection. CoRR (2020)
Cecilia Ferrando, Shufan Wang, Daniel Sheldon
General-Purpose Differentially-Private Confidence Intervals. CoRR (2020)
Zezhou Cheng, Saadia Gabriel, Pankaj Bhambhani, Daniel Sheldon, Subhransu Maji, Andrew Laughlin, David Winkler
Detecting and Tracking Communal Bird Roosts in Weather Radar Data. AAAI (2020)
Edmond Cunningham, Renos Zabounidis, Abhinav Agrawal, Ina Fiterau, Daniel Sheldon
Normalizing Flows Across Dimensions. CoRR (2020)
Ryan McKenna, Daniel Sheldon, Gerome Miklau
Graphical-model based estimation and inference for differential privacy. CoRR (2019)
Shiv Shankar, Daniel Sheldon, Tao Sun, John Pickering, Thomas G. Dietterich
Three-quarter Sibling Regression for Denoising Observational Data. IJCAI (2019)
Justin Domke, Daniel Sheldon
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation. CoRR (2019)
Carla P. Gomes, Thomas G. Dietterich, Christopher Barrett, Jon Conrad, Bistra Dilkina, Stefano Ermon, Fei Fang, Andrew Farnsworth, Alan Fern, Xiaoli Z. Fern, Daniel Fink, Douglas H. Fisher, Alexander Flecker, Daniel Freund, Angela Fuller, John M. Gregoire, John E. Hopcroft, Steve Kelling, J. Zico Kolter, Warren B. Powell, Nicole D. Sintov, John S. Selker, Bart Selman, Daniel Sheldon, David B. Shmoys, Milind Tambe, Weng-Keen Wong, Christopher Wood, Xiaojian Wu, Yexiang Xue, Amulya Yadav, Abdul-Aziz Yakubu, Mary Lou Zeeman
Computational sustainability: computing for a better world and a sustainable future. Commun. ACM 62 (9) (2019)
Garrett Bernstein, Daniel Sheldon
Differentially Private Bayesian Linear Regression. CoRR (2019)
Zezhou Cheng, Matheus Gadelha, Subhransu Maji, Daniel Sheldon
A Bayesian Perspective on the Deep Image Prior. CoRR (2019)
Ryan McKenna, Daniel Sheldon, Gerome Miklau
Graphical-model based estimation and inference for differential privacy. ICML (2019)
Zezhou Cheng, Matheus Gadelha, Subhransu Maji, Daniel Sheldon
A Bayesian Perspective on the Deep Image Prior. CVPR (2019)
Daniel Sheldon, Kevin Winner, Debora Sujono
Learning in Integer Latent Variable Models with Nested Automatic Differentiation. ICML (2018)
Srinivasan Iyengar, Stephen Lee, Daniel Sheldon, Prashant J. Shenoy
SolarClique: Detecting Anomalies in Residential Solar Arrays. COMPASS (2018)
Justin Domke, Daniel Sheldon
Importance Weighting and Variational Inference. CoRR (2018)
Garrett Bernstein, Daniel Sheldon
Differentially Private Bayesian Inference for Exponential Families. CoRR (2018)
Daniel Sheldon, Kevin Winner, Debora Sujono
Learning in Integer Latent Variable Models with Nested Automatic Differentiation. CoRR (2018)
Kevin Winner, Debora Sujono, Daniel Sheldon
Exact Inference for Integer Latent-Variable Models. ICML (2017)
Tao Sun, Daniel Sheldon, Brendan OConnor
A Probabilistic Approach for Learning with Label Proportions Applied to the US Presidential Election. ICDM (2017)
Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau
Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models. CoRR (2017)
XiaoJian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein
Robust Optimization for Tree-Structured Stochastic Network Design. AAAI (2017)
Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau
Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models. ICML (2017)
Christen H. Fleming, Daniel Sheldon, Eliezer Gurarie, William F. Fagan, Scott LaPoint, Justin M. Calabrese
Kálmán filters for continuous-time movement models. Ecol. Informatics 40 (2017)
Garrett Bernstein, Daniel Sheldon
Consistently Estimating Markov Chains with Noisy Aggregate Data. CoRR (2016)
Garrett Bernstein, Daniel Sheldon
Consistently Estimating Markov Chains with Noisy Aggregate Data. AISTATS (2016)
XiaoJian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein
Robust Optimization for Tree-Structured Stochastic Network Design. CoRR (2016)
Aruni Roy Chowdhury, Daniel Sheldon, Subhransu Maji, Erik G. Learned-Miller
Distinguishing Weather Phenomena from Bird Migration Patterns in Radar Imagery. CVPR Workshops (2016)
XiaoJian Wu, Daniel Sheldon, Shlomo Zilberstein
Optimizing Resilience in Large Scale Networks. AAAI (2016)
Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, Daniel Sheldon
Approximate Inference Using DC Programming For Collective Graphical Models. AISTATS (2016)
Akshat Kumar, Arambam James Singh, Pradeep Varakantham, Daniel Sheldon
Robust Decision Making for Stochastic Network Design. AAAI (2016)
Shan Xue, Alan Fern, Daniel Sheldon
Scheduling Conservation Designs for Maximum Flexibility via Network Cascade Optimization. J. Artif. Intell. Res. 52 (2015)
Tao Sun, Daniel Sheldon, Akshat Kumar
Message Passing for Collective Graphical Models. ICML (2015)
Kevin Winner, Garrett Bernstein, Daniel Sheldon
Inference in a Partially Observed Queuing Model with Applications in Ecology. ICML (2015)
Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum
Bethe Projections for Non-Local Inference. CoRR (2015)
Chia-Jung Lee, Qingyao Ai, W. Bruce Croft, Daniel Sheldon
An Optimization Framework for Merging Multiple Result Lists. CIKM (2015)
XiaoJian Wu, Daniel Sheldon, Shlomo Zilberstein
Fast Combinatorial Algorithm for Optimizing the Spread of Cascades. IJCAI (2015)
Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum
Bethe Projections for Non-Local Inference. UAI (2015)
Shan Xue, Alan Fern, Daniel Sheldon
Dynamic Resource Allocation for Optimizing Population Diffusion. AISTATS (2014)
Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich
Gaussian Approximation of Collective Graphical Models. CoRR (2014)
Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich
Gaussian Approximation of Collective Graphical Models. ICML (2014)
XiaoJian Wu, Daniel Sheldon, Shlomo Zilberstein
Rounded Dynamic Programming for Tree-Structured Stochastic Network Design. AAAI (2014)
Andrew Farnsworth, Daniel Sheldon, Jeffrey Geevarghese, Jed Irvine, Benjamin Van Doren, Kevin F. Webb, Thomas G. Dietterich, Steve Kelling
Reconstructing Velocities of Migrating Birds from Weather Radar - A Case Study in Computational Sustainability. AI Mag. 35 (2) (2014)
Daniel Sheldon, Tao Sun, Akshat Kumar, Thomas G. Dietterich
Approximate Inference in Collective Graphical Models. ICML (3) (2013)
Akshat Kumar, Daniel Sheldon, Biplav Srivastava
Collective Diffusion Over Networks: Models and Inference. UAI (2013)
XiaoJian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein
Parameter Learning for Latent Network Diffusion. IJCAI (2013)
Daniel Sheldon, Neal E. Young
Hamming Approximation of NP Witnesses. Theory Comput. 9 (2013)
Akshat Kumar, Daniel Sheldon, Biplav Srivastava
Collective Diffusion Over Networks: Models and Inference. CoRR (2013)
Thilanka Appuhamillage, Daniel Sheldon
First Passage Time of Skew Brownian Motion. J. Appl. Probab. 49 (3) (2012)
Daniel Sheldon, Neal E. Young
Hamming Approximation of NP Witnesses CoRR (2012)
Shan Xue, Alan Fern, Daniel Sheldon
Scheduling Conservation Designs via Network Cascade Optimization. AAAI (2012)
Daniel Sheldon, Bistra Dilkina, Adam N. Elmachtoub, Ryan Finseth, Ashish Sabharwal, Jon Conrad, Carla P. Gomes, David B. Shmoys, William Allen, Ole Amundsen, William Vaughan
Maximizing the Spread of Cascades Using Network Design CoRR (2012)
Daniel Sheldon, Milad Shokouhi, Martin Szummer, Nick Craswell
LambdaMerge: merging the results of query reformulations. WSDM (2011)
Daniel Sheldon, Bistra Dilkina, Adam N. Elmachtoub, Ryan Finseth, Ashish Sabharwal, Jon Conrad, Carla P. Gomes, David B. Shmoys, William Allen, Ole Amundsen, William Vaughan
Maximizing the Spread of Cascades Using Network Design. UAI (2010)
John E. Hopcroft, Daniel Sheldon
Manipulation-Resistant Reputations Using Hitting Time. Internet Math. 5 (1) (2008)
Daniel Sheldon, M. A. Saleh Elmohamed, Dexter Kozen
Collective Inference on Markov Models for Modeling Bird Migration. NIPS (2007)
John E. Hopcroft, Daniel Sheldon
Manipulation-Resistant Reputations Using Hitting Time. WAW (2007)